My Python coding for labs and applied exercises in the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani.
Chapter 3 - Linear Regression
Chapter 4 - Classification
Chapter 5 - Resampling Methods
Chapter 6 - Linear Model Selection and Regularization
Chapter 7 - Moving Beyond Linearity
Chapter 8 - Tree-Based Methods
Chapter 9 - Support Vector Machines
Chapter 10 - Unsupervised Learning
Development environment:
- Anaconda 4.3.1 for macOS, with Python 3.6
- Jupyter Notebook 5.0.0
- Emacs 25.1 with Emacs IPython Notebook
Python libraries used:
- scikit-learn
- statsmodels
- pandas
- patsy
- numpy
- scipy
- matplotlib
- seaborn
Reference: Elements of Statistical Learning by Hastie, T., Tibshirani, R., Friedman, J.